Based on traditional SIFT feature point matching method and image pixel level fusion
an improved SIFT feature matching algorithm is proposed. Firstly the algorithm in the feature extraction process with Harris corner detector and picks out robust points; Followed use of 48 dimensional vector based on a circular window to dimension reduction and the feature points are refined by the geometrical consistency of the matching feature points
to improve the efficiency of the algorithm. Finally
the overlapped area linear transition fusion algorithm of image smoothing the transition to eliminate the splicing gap. The experiment verifies the robustness and rapidity of the proposed method.
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references
Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110. DOI: 91
Ke Y, Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors[C]// Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition. Washington DC, USA, 2004: 511-517.